Self-adaptive adjustment method and system for measurement of electric energy meter

Abstract

The invention discloses a self-adaptive adjustment method and system for electric energy meter measurement, and relates to the technical field of electric power metering, and the method comprises the steps: building a first measurement mode and a second measurement mode for a target electric energy meter; carrying out load fluctuation monitoring on the target electric energy meter through a load monitoring module, and generating a load fluctuation index; if the load fluctuation index is greater than the preset fluctuation index, activating a first measurement correction module to perform measurement precision adjustment on the target electric energy meter according to a first measurement mode; and if the load fluctuation index is smaller than or equal to the preset fluctuation index, activating a second measurement correction module to perform measurement precision adjustment on the target electric energy meter according to a second measurement mode. The technical problem that the measurement mode cannot be adaptively adjusted according to different loads during measurement of the electric energy meter in the prior art is solved, adaptive adjustment of the measurement of the electric energy meter is realized, and the technical effect of balancing the power consumption and precision of the measurement of the electric energy meter is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power metering, and in particular to an adaptive adjustment method and system for electric energy meter measurement. Background Art

[0002] With the rapid development of the power system, electric energy meters, as key equipment for power metering, are widely used in industrial, commercial and household scenarios. Traditional electric energy meters operate at a fixed sampling frequency and fixed measurement mode. Although they can ensure metering accuracy when the load fluctuates drastically or the power grid environment is complex, they still maintain high sampling frequency and accuracy requirements under conditions of stable load and small environmental changes, resulting in unnecessary energy consumption. At the same time, with the development of smart grid technology, users have higher and higher requirements for the accuracy, power consumption, and adaptability of electric energy meters. Traditional electric energy meters can no longer meet the needs of automatically adjusting the measurement mode in scenarios with frequent or small load fluctuations. Summary of the invention

[0003] The present application provides an adaptive adjustment method and system for electric energy meter measurement, which is used to solve the technical problem in the prior art that the measurement mode cannot be adaptively adjusted according to different loads when measuring the electric energy meter.

[0004] In view of the above problems, the present application provides an adaptive adjustment method and system for electric energy meter measurement.

[0005] In a first aspect of the present application, a method for adaptively adjusting electric energy meter measurement is provided, the method comprising: A first measurement mode and a second measurement mode are established for a target electric energy meter, wherein the first measurement mode is a high-precision mode and the second measurement mode is a low-energy consumption mode; load fluctuation of the target electric energy meter is monitored by a load monitoring module to generate a load fluctuation index; if the load fluctuation index is greater than a preset fluctuation index, the first measurement correction module is activated according to the first measurement mode to adjust the measurement accuracy of the target electric energy meter; if the load fluctuation index is less than or equal to the preset fluctuation index, the second measurement correction module is activated according to the second measurement mode to adjust the measurement accuracy of the target electric energy meter.

[0006] A second aspect of the present application provides an adaptive regulation system for electric energy meter measurement, the system comprising: A measurement mode establishment module, wherein the measurement mode establishment module establishes a first measurement mode and a second measurement mode for a target electric energy meter, wherein the first measurement mode is a high-precision mode and the second measurement mode is a low-energy consumption mode; a load fluctuation monitoring module, wherein the load fluctuation monitoring module monitors the load fluctuation of the target electric energy meter through a load monitoring module and generates a load fluctuation index; a first measurement accuracy adjustment module, wherein the first measurement accuracy adjustment module is used to activate the first measurement correction module according to the first measurement mode to adjust the measurement accuracy of the target electric energy meter if the load fluctuation index is greater than a preset fluctuation index; a second measurement accuracy adjustment module, wherein the second measurement accuracy adjustment module is used to activate the second measurement correction module according to the second measurement mode to adjust the measurement accuracy of the target electric energy meter if the load fluctuation index is less than or equal to the preset fluctuation index.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application establishes a first measurement mode and a second measurement mode for a target electric energy meter, wherein the first measurement mode is a high-precision mode and the second measurement mode is a low-energy consumption mode; the load fluctuation of the target electric energy meter is monitored by a load monitoring module to generate a load fluctuation index; if the load fluctuation index is greater than a preset fluctuation index, the first measurement correction module is activated according to the first measurement mode to adjust the measurement accuracy of the target electric energy meter; if the load fluctuation index is less than or equal to the preset fluctuation index, the second measurement correction module is activated according to the second measurement mode to adjust the measurement accuracy of the target electric energy meter. The present invention solves the technical problem that the prior art cannot adaptively adjust the measurement mode according to different loads when measuring an electric energy meter. By establishing a high-precision mode and a low-energy consumption mode, the measurement mode is dynamically switched according to the load fluctuation monitoring result. If the load fluctuation is large, the high-precision mode is activated to improve the measurement accuracy. If the load is stable, the low-energy consumption mode is activated to reduce energy consumption, so as to realize adaptive adjustment of the electric energy meter measurement and achieve the technical effect of balancing the power consumption and accuracy of the electric energy meter measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic flow chart of an adaptive adjustment method for electric energy meter measurement provided in an embodiment of the present application.

[0010] Figure 2A schematic diagram of the structure of an adaptive adjustment system for electric energy meter measurement provided in an embodiment of the present application.

[0011] Description of the reference numerals: measurement mode establishing module 11 , load fluctuation monitoring module 12 , first measurement accuracy adjusting module 13 , second measurement accuracy adjusting module 14 . DETAILED DESCRIPTION

[0012] The present application provides an adaptive adjustment method and system for electric energy meter measurement, aiming to solve the technical problem in the prior art that the measurement mode cannot be adaptively adjusted according to different loads when measuring the electric energy meter. By establishing a high-precision mode and a low-energy consumption mode, the measurement mode is dynamically switched according to the load fluctuation monitoring results. If the load fluctuation is large, the high-precision mode is activated to improve the measurement accuracy. If the load is stable, the low-energy consumption mode is activated to reduce energy consumption, thereby realizing adaptive adjustment of the electric energy meter measurement and achieving the technical effect of balancing the power consumption and accuracy of the electric energy meter measurement.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0015] Embodiment 1, as Figure 1 As shown, the present application provides an adaptive adjustment method for electric energy meter measurement, the method comprising: Step S100: establishing a first measurement mode and a second measurement mode for a target electric energy meter, wherein the first measurement mode is a high-precision mode and the second measurement mode is a low-energy consumption mode.

[0016] In an embodiment of the present application, in order to realize adaptive measurement and adjustment of the electric energy meter, two measurement modes are first established, namely the first measurement mode and the second measurement mode. The first measurement mode is a high-precision mode, which is suitable for scenarios with large load fluctuations, high harmonic content or unstable power environment. This mode improves accuracy by increasing the use of data acquisition and processing resources. The second measurement mode is a low-energy consumption mode. When the power environment is relatively stable and the load fluctuation is small, the electric energy meter switches to the low-energy consumption mode. The main feature of the low-energy consumption mode is to reduce energy consumption and extend the service life of the electric energy meter by reducing the sampling frequency and simplifying the algorithm. The threshold of the load fluctuation is pre-set by technical experts. When it is greater than the threshold, it is the first measurement mode, and when it is less than or equal to the threshold, it is the second measurement mode.

[0017] Step S200: monitoring the load fluctuation of the target electric energy meter through a load monitoring module to generate a load fluctuation index.

[0018] In an embodiment of the present application, when the load fluctuation monitoring of the target electric energy meter is performed by the load monitoring module, the integrated sensor in the load monitoring module is first used to collect data such as voltage, current, active power, reactive power and power factor in real time. These data are then preprocessed to remove noise and outliers to ensure data quality. Next, the fluctuation amplitude is calculated for each parameter, and the fluctuation is determined by comparing the current value with the historical average. Afterwards, these fluctuation amplitudes are weighted and fused, and different weights are assigned to different parameters. Finally, a comprehensive load fluctuation index is generated.

[0019] Furthermore, in the method provided in the embodiment of the application, the load fluctuation monitoring module is used to monitor the load fluctuation of the target electric energy meter to generate a load fluctuation index, and the method further includes: The target electric energy meter is monitored by the integrated sensor in the load monitoring module to generate load fluctuation monitoring data; wherein the load fluctuation monitoring data includes voltage monitoring data, current monitoring data, active power monitoring data, reactive power monitoring data and power factor monitoring data; the fluctuation amplitudes of the voltage monitoring data, the current monitoring data, the active power monitoring data, the reactive power monitoring data and the power factor monitoring data are calculated respectively and then weightedly fused to generate the load fluctuation index.

[0020] In the embodiment of the present application, the target electric energy meter is first monitored by the integrated sensor in the load monitoring module, wherein the integrated sensor includes a voltage sensor and a current sensor, and voltage monitoring data, current monitoring data, active power monitoring data, reactive power monitoring data and power factor monitoring data are obtained through monitoring. These data together constitute the load fluctuation monitoring data.

[0021] Next, the fluctuation amplitude is calculated for the voltage monitoring data, current monitoring data, active power monitoring data, reactive power monitoring data and power factor monitoring data respectively. For example, when calculating the voltage fluctuation amplitude, Calculate, where and are the maximum voltage and minimum voltage within the pre-specified time window, is the standard voltage value, such as 220V or 380V. The same process applies to the current fluctuation amplitude, active power fluctuation amplitude, reactive power fluctuation amplitude and power factor fluctuation amplitude. The fluctuation amplitude of each parameter is calculated by extracting the maximum and minimum values ​​within the time window and comparing them with the standard value.

[0022] Next, the fluctuation amplitudes of all monitoring data are weighted and fused, wherein the weight of the fluctuation amplitude of each monitoring data is preset, and the load fluctuation index is obtained through weighted fusion.

[0023] Step S300: If the load fluctuation index is greater than a preset fluctuation index, activating a first measurement correction module according to the first measurement mode to adjust the measurement accuracy of the target electric energy meter.

[0024] In an embodiment of the present application, when the load fluctuation index exceeds a preset threshold, it switches to a high-precision mode and activates the first measurement correction module. This module adjusts the measurement accuracy through a high-frequency self-calibration compensation branch and a high-order harmonic analysis branch. The self-calibration compensation branch generates an error compensation coefficient for correcting the measurement results by real-time detection of external environmental factors such as temperature, humidity, and electromagnetic interference. At the same time, the high-order harmonic analysis branch identifies the high-order harmonics in the voltage and current through Fourier transform, analyzes their impact on the measurement accuracy and compensates for them, and finally completes the measurement accuracy adjustment of the electric energy meter.

[0025] Furthermore, in the method provided in the embodiment of the application, if the load fluctuation index is greater than the preset fluctuation index, activating the first measurement correction module according to the first measurement mode to adjust the measurement accuracy of the target electric energy meter also includes: The first measurement correction module includes a high-frequency self-calibration compensation branch and a high-order harmonic analysis branch; the high-frequency self-calibration compensation branch performs external environment parameter detection and metering error influence analysis at a first preset frequency to generate a first self-calibration error; the high-order harmonic analysis branch performs an electric energy meter measurement error analysis under high-order harmonics based on preset high-order harmonics to generate a first harmonic influence error; the metering result of the target electric energy meter is error compensated using the first self-calibration error and the first harmonic influence error to complete the measurement accuracy adjustment.

[0026] In an embodiment of the present application, the first measurement correction module includes a high-frequency self-calibration compensation branch and a high-order harmonic analysis branch. When the first measurement correction module is activated, the external environmental parameters are first detected at a preset high frequency through the high-frequency self-calibration compensation branch. These external environmental parameters usually include temperature, humidity, and electromagnetic interference, etc. These factors have a significant impact on the measurement accuracy of the electric energy meter. Based on existing multivariate regression analysis or machine learning technology, the error impact analysis of these external factors is performed to generate a first self-calibration error.

[0027] Then, the high-order harmonics in the power system are analyzed through the high-order harmonic analysis branch. High-order harmonics are voltage and current distortions caused by nonlinear loads, and harmonics include 3rd, 5th, 7th harmonics and 9th, 11th, 13th and other high-order harmonics. Frequency domain analysis techniques such as Fourier transform are used to identify these harmonic components, analyze their impact on the measurement of the electric energy meter, and generate the first harmonic impact error.

[0028] Finally, the first self-calibration error generated by the high-frequency self-calibration compensation branch is combined with the first harmonic influence error generated by the high-order harmonic analysis branch, and the two types of errors are integrated by weighted averaging, where each error has the same weight, and then the measurement result of the electric energy meter is compensated for the error according to the weighted result. Through the comprehensive correction of the error, it is ensured that the electric energy meter can maintain high-precision measurement in a complex power environment, thereby completing the measurement accuracy adjustment.

[0029] Furthermore, in the method provided in the embodiment of the application, the high-frequency self-calibration compensation branch performs external environment parameter detection and measurement error influence analysis at a first preset frequency to generate a first self-calibration error, and further includes: External environmental parameter detection is performed at the first preset frequency, wherein the external environmental parameters include ambient temperature, ambient humidity and ambient electromagnetic interference; the ambient temperature, ambient humidity and ambient electromagnetic interference are input into the high-frequency self-calibration compensation branch to perform environmental error impact analysis to generate the first self-calibration error; wherein the high-frequency self-calibration compensation branch is trained through historical environmental error impact samples.

[0030] In the embodiment of the present application, the external environment parameter detection is first performed according to the first preset frequency. The first preset frequency refers to the time interval or frequency for performing external environment detection and calibration in high-precision mode, which is preset by technical experts. When performing external environment parameter detection, the external environment is monitored in real time through built-in sensors such as temperature sensors, humidity sensors and electromagnetic interference detectors to collect ambient temperature, ambient humidity and electromagnetic interference data.

[0031] Next, the acquired external environmental parameters are input into the pre-trained high-frequency self-calibration compensation branch for environmental error impact analysis to generate the first self-calibration error. The high-frequency self-calibration compensation branch is trained by historical environmental error impact samples. Specifically, during training, historical environmental error impact samples and corresponding calibration errors are obtained through the historical database. Among them, the historical environmental error impact samples include environmental parameters under various temperatures, humidity and electromagnetic interference levels. The calibration error data is obtained through precise measurement and calibration on site. Under different environmental conditions, the error between the measurement result and the standard measurement value is recorded as the corresponding calibration error. The training process of the high-frequency self-calibration compensation branch is carried out through a multivariate regression model. During training, the input data of the model includes historical environmental error impact samples and corresponding calibration errors. During the training process, the model continuously optimizes its weights and parameters by inputting a large amount of historical data, and learns the relationship between environmental conditions and measurement errors. Finally, after multiple training and optimization, a high-frequency self-calibration compensation branch is obtained.

[0032] Furthermore, in the method provided in the embodiment of the application, the electric energy meter measurement error analysis under the high-order harmonics is performed based on the preset high-order harmonics by the high-order harmonic analysis branch to generate the first harmonic influence error, and further includes: The first measurement and correction module also includes a high sampling frequency switching branch, which is connected to a high-precision sampling frequency mapping table and is used to traverse the high-precision sampling frequency mapping table to match high sampling frequency parameters when the load fluctuation index is greater than a preset fluctuation index, and generate a high-frequency sampling frequency; control the load monitoring module to collect voltage waveforms and current waveforms according to the high-frequency sampling frequency, input the voltage waveform and the current waveform into the high-order harmonic analysis branch to identify harmonic components and analyze measurement errors, and generate the first harmonic influence error.

[0033] In the embodiment of the present application, when it is detected that the load fluctuation index is greater than the preset threshold, the high sampling frequency switching branch in the first measurement correction module is automatically started, and the high-precision sampling frequency mapping table is accessed by the table lookup method. The table pre-stores the optimal sampling frequency under different load fluctuation conditions, and the table is traversed according to the current load fluctuation situation to match the most suitable sampling frequency to generate a high-frequency sampling frequency.

[0034] Once the high-frequency sampling frequency is matched, adjust the sampling frequency of the load monitoring module. The load monitoring module contains voltage and current sensors, which capture the rapid changes of voltage and current waveforms through high-frequency sampling. In this step, the high-frequency data of the voltage and current waveforms are collected and digitized through the ADC.

[0035] Next, the collected voltage and current waveforms are input into the high-order harmonic analysis branch, which uses Fourier transform to convert the time domain waveform into a frequency domain signal and identify the high-order harmonic components in the waveform, such as the 3rd, 5th, and 7th harmonics. After the amplitude and phase information of these harmonics are extracted through Fourier transform, they are input into the harmonic error analysis model trained based on historical data for error analysis, and the measurement error caused by the high-order harmonics is calculated, that is, the first harmonic influence error is generated.

[0036] Furthermore, in the method provided in the embodiment of the application, the voltage waveform and the current waveform are input into the high-order harmonic analysis branch to perform harmonic component identification and measurement error analysis to generate the first harmonic influence error, and further includes: Perform frequency domain signal transformation on the voltage waveform and the current waveform to generate frequency domain voltage signal and frequency domain current signal; extract the amplitude and phase corresponding to the preset higher harmonic according to the frequency domain voltage signal and the frequency domain current signal to generate preset higher harmonic frequency components, wherein the preset higher harmonics include at least 3rd harmonic, 5th harmonic, 7th harmonic, 9th harmonic and 11th harmonic; input the preset higher harmonic frequency components into the higher harmonic analysis branch for error analysis to generate the first harmonic influence error; wherein the higher harmonic analysis branch is trained by preset higher harmonic frequency samples and corresponding error samples.

[0037] In the embodiment of the present application, firstly, the time domain signals of the voltage and current waveforms collected from the ADC are converted into frequency domain signals through the fast Fourier transform technology to generate frequency domain voltage signals and frequency domain current signals.

[0038] After completing the frequency domain signal transformation, the preset high-order harmonic frequency is matched. First, according to the pre-set high-order harmonic frequency, such as the 3rd, 5th, 7th, 9th, and 11th harmonics, the amplitude and phase of the corresponding frequency are extracted from the frequency domain signal. For matching, the frequency filtering method is used to filter out the frequency components that match the preset harmonic frequency from the frequency domain signal. Frequency filtering is a common signal processing technology. By pre-setting a threshold or frequency range, only the components of the target harmonic frequency are retained and other non-target frequency components are filtered out. Specifically, the frequency points such as the 3rd harmonic, 5th harmonic, 7th harmonic, 9th harmonic, and 11th harmonic are located in the frequency domain signal, and the amplitude and phase corresponding to these frequencies are extracted. The amplitude represents the strength of the harmonic in the power signal, while the phase represents the position of the harmonic on the time axis. Through this process, preset high-order harmonic frequency components are generated. These components contain the amplitude and phase information of each target harmonic and represent the harmonic characteristics in the power system.

[0039] The obtained preset high-order harmonic frequency components are then input into the high-order harmonic analysis branch. This analysis branch relies on a large amount of historical data for training, which includes preset high-order harmonic frequency samples and corresponding error samples. In order to train the model, multivariate regression analysis and machine learning models such as support vector machines are used. During the training process, the linear relationship between harmonic frequency, amplitude and electric energy meter measurement error is identified by multivariate regression analysis, while for more complex nonlinear relationships, support vector machines are used for training. The training data of the model comes from long-term measured data samples, covering high-order harmonic frequencies and corresponding measurement errors under various load conditions, which are obtained from the historical database. After training, the high-order harmonic analysis branch is able to quickly evaluate the impact of these high-order harmonics on the measurement results of the electric energy meter when new harmonic frequency components are input. The generated preset high-order harmonic frequency components are input into the analysis model, and the impact of each high-order harmonic on the measurement accuracy is obtained by calculating the correlation between the error samples and the frequency samples, and the first harmonic influence error is generated.

[0040] Step S400: If the load fluctuation index is less than or equal to a preset fluctuation index, activating a second measurement correction module according to the second measurement mode to adjust the measurement accuracy of the target electric energy meter.

[0041] In an embodiment of the present application, when it is detected that the load fluctuation index is less than or equal to a preset value, it switches to the second measurement mode to optimize energy consumption and maintain the necessary measurement accuracy. Specifically, first, the sampling frequency is reduced by switching the branch with a low sampling frequency, thereby reducing the acquisition frequency of the voltage and current signals and reducing energy consumption. In this mode, the low-frequency self-calibration compensation branch is activated to monitor external environmental parameters such as temperature, humidity, electromagnetic interference, etc. at a lower frequency, and a second self-calibration error is generated to compensate for the impact of the external environment on the measurement accuracy. At the same time, the basic harmonic analysis branch extracts and analyzes the amplitude and phase of the third and fifth basic harmonics, evaluates their impact on the measurement results, and generates a harmonic influence error. Finally, the measurement accuracy is adjusted according to the generated error to ensure that the electric energy meter can still maintain accurate measurement results under low sampling frequencies and small load fluctuations.

[0042] Furthermore, in the method provided in the embodiment of the application, if the load fluctuation index is less than or equal to the preset fluctuation index, activating the second measurement correction module according to the second measurement mode to adjust the measurement accuracy of the target electric energy meter also includes: The second measurement correction module includes a low sampling frequency switching branch, a low frequency self-calibration compensation branch and a basic harmonic analysis branch; wherein the low sampling frequency switching branch corresponds to a fixed low sampling frequency; the low frequency self-calibration compensation branch corresponds to a second preset frequency, and the second preset frequency is lower than the first preset frequency; the basic harmonic analysis branch performs harmonic component identification and measurement error analysis according to preset basic harmonics, wherein the preset basic harmonics include at least 3rd harmonics and 5th harmonics.

[0043] In an embodiment of the present application, the measurement accuracy of the electric energy meter is adjusted by the second measurement correction module in the second measurement mode. The second measurement correction module includes a low sampling frequency switching branch, a low frequency self-calibration compensation branch and a basic harmonic analysis branch. When the load fluctuation is small and stable, the sampling frequency is reduced by the low sampling frequency switching branch, and the frequency of data acquisition is reduced, thereby achieving energy consumption optimization. Specifically, when it is detected that the load fluctuation index is less than or equal to the preset value, the low sampling frequency switching branch reduces the acquisition frequency of the voltage and current signals by using a fixed low sampling frequency, and reduces the sampling frequency to a fixed value, such as 500Hz, to reduce unnecessary energy consumption. This is different from the high sampling frequency in the high-precision mode. When the load fluctuation is small and stable, the low sampling frequency can meet the basic measurement requirements while significantly optimizing energy consumption.

[0044] At the same time, the influence of the external environment is adjusted through the low-frequency self-calibration compensation branch. In high-precision mode, self-calibration is performed frequently to cope with changes in environmental conditions, but in low-energy consumption mode, the frequency of self-calibration is greatly reduced. Through a delayed trigger mechanism or a change monitoring mechanism, self-calibration is performed only when significant changes in environmental parameters are detected. In specific operations, external conditions are monitored in real time through environmental sensors, and the self-calibration procedure is only started when changes in parameters such as temperature, humidity, and electromagnetic interference exceed a certain threshold. For example, when a temperature change of more than 3°C, a humidity change of more than 5%, or an increase in electromagnetic interference reaches a preset limit, a self-calibration operation is triggered. During the calibration process, similar to the process of generating the first self-calibration error, an error correction model is trained to analyze the impact of environmental changes on the measurement results and generate a second self-calibration error. The error correction model is trained with environmental change data at a low sampling frequency and the corresponding error data.

[0045] Next, the harmonic detection process is simplified through the basic harmonic analysis branch. In high-precision mode, high-order harmonics such as the 7th and 9th harmonics are detected and analyzed, but in low-energy mode, only basic harmonics such as the 3rd and 5th harmonics are analyzed. The voltage and current signals collected by low-frequency ADC sampling are converted into frequency domain signals using fast Fourier transform to extract the frequency components corresponding to the preset basic harmonic frequency. Using frequency screening technology, focus on the 3rd harmonic (150Hz) and 5th harmonic (250Hz), and ignore other high-order harmonics to reduce the computational burden and energy consumption. Then, based on the pre-trained basic harmonic error model, the impact of these harmonic components on the measurement results is evaluated to generate harmonic impact errors. The basic harmonic error model is similar to the training process of the high-order harmonic analysis branch. The model is trained based on basic harmonic data under low sampling frequency conditions, including the frequency components, amplitudes and phases of the 3rd and 5th harmonics, and the impact of these harmonics on the measurement results of the electric energy meter in low-energy mode.

[0046] Finally, the obtained second self-calibration error and the harmonic influence error generated by the basic harmonic analysis branch are combined through the weighted average method to obtain a combined total error, and the measurement accuracy of the target electric energy meter is adjusted according to the generated total error.

[0047] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: The present application establishes a first measurement mode and a second measurement mode for a target electric energy meter, wherein the first measurement mode is a high-precision mode and the second measurement mode is a low-energy consumption mode; the load fluctuation of the target electric energy meter is monitored by a load monitoring module to generate a load fluctuation index; if the load fluctuation index is greater than a preset fluctuation index, the first measurement correction module is activated according to the first measurement mode to adjust the measurement accuracy of the target electric energy meter; if the load fluctuation index is less than or equal to the preset fluctuation index, the second measurement correction module is activated according to the second measurement mode to adjust the measurement accuracy of the target electric energy meter. The present invention solves the technical problem that the prior art cannot adaptively adjust the measurement mode according to different loads when measuring an electric energy meter. By establishing a high-precision mode and a low-energy consumption mode, the measurement mode is dynamically switched according to the load fluctuation monitoring result. If the load fluctuation is large, the high-precision mode is activated to improve the measurement accuracy. If the load is stable, the low-energy consumption mode is activated to reduce energy consumption, so as to realize adaptive adjustment of the electric energy meter measurement and achieve the technical effect of balancing the power consumption and accuracy of the electric energy meter measurement.

[0048] Embodiment 2, based on the same inventive concept as the adaptive adjustment method for measuring an electric energy meter in the above embodiment, Figure 2As shown, the present application provides an adaptive adjustment system for electric energy meter measurement, and the system and method embodiments in the present application are based on the same inventive concept. The system includes: A measurement mode establishing module 11, wherein the measurement mode establishing module 11 establishes a first measurement mode and a second measurement mode for a target electric energy meter, wherein the first measurement mode is a high-precision mode and the second measurement mode is a low-energy consumption mode; a load fluctuation monitoring module 12, wherein the load fluctuation monitoring module 12 monitors the load fluctuation of the target electric energy meter through a load monitoring module to generate a load fluctuation index; a first measurement accuracy adjustment module 13, wherein the first measurement accuracy adjustment module 13 is used to activate the first measurement correction module according to the first measurement mode to adjust the measurement accuracy of the target electric energy meter if the load fluctuation index is greater than a preset fluctuation index; a second measurement accuracy adjustment module 14, wherein the second measurement accuracy adjustment module 14 is used to activate the second measurement correction module according to the second measurement mode to adjust the measurement accuracy of the target electric energy meter if the load fluctuation index is less than or equal to the preset fluctuation index.

[0049] Furthermore, the system is also used to implement the following functions: The target electric energy meter is monitored by the integrated sensor in the load monitoring module to generate load fluctuation monitoring data; wherein the load fluctuation monitoring data includes voltage monitoring data, current monitoring data, active power monitoring data, reactive power monitoring data and power factor monitoring data; the fluctuation amplitudes of the voltage monitoring data, the current monitoring data, the active power monitoring data, the reactive power monitoring data and the power factor monitoring data are calculated respectively and then weightedly fused to generate the load fluctuation index.

[0050] Furthermore, the system is also used to implement the following functions: The first measurement correction module includes a high-frequency self-calibration compensation branch and a high-order harmonic analysis branch; the high-frequency self-calibration compensation branch performs external environment parameter detection and metering error influence analysis at a first preset frequency to generate a first self-calibration error; the high-order harmonic analysis branch performs an electric energy meter measurement error analysis under high-order harmonics based on preset high-order harmonics to generate a first harmonic influence error; the metering result of the target electric energy meter is error compensated using the first self-calibration error and the first harmonic influence error to complete the measurement accuracy adjustment.

[0051] Furthermore, the system is also used to implement the following functions: Performing external environmental parameter detection at the first preset frequency, wherein the external environmental parameters include environmental temperature, environmental humidity, and environmental electromagnetic interference; inputting the environmental temperature, the environmental humidity, and the environmental electromagnetic interference into the high-frequency self-calibration compensation branch to perform environmental error impact analysis to generate the first self-calibration error; The high-frequency self-calibration compensation branch is trained by influencing samples through historical environmental errors.

[0052] Furthermore, the system is also used to implement the following functions: The first measurement and correction module also includes a high sampling frequency switching branch, which is connected to a high-precision sampling frequency mapping table and is used to traverse the high-precision sampling frequency mapping table to match high sampling frequency parameters when the load fluctuation index is greater than a preset fluctuation index, and generate a high-frequency sampling frequency; control the load monitoring module to collect voltage waveforms and current waveforms according to the high-frequency sampling frequency, input the voltage waveform and the current waveform into the high-order harmonic analysis branch to identify harmonic components and analyze measurement errors, and generate the first harmonic influence error.

[0053] Furthermore, the system is also used to implement the following functions: Perform frequency domain signal transformation on the voltage waveform and the current waveform to generate frequency domain voltage signal and frequency domain current signal; extract the amplitude and phase corresponding to the preset higher harmonic according to the frequency domain voltage signal and the frequency domain current signal to generate preset higher harmonic frequency components, wherein the preset higher harmonics include at least 3rd harmonic, 5th harmonic, 7th harmonic, 9th harmonic and 11th harmonic; input the preset higher harmonic frequency components into the higher harmonic analysis branch for error analysis to generate the first harmonic influence error; wherein the higher harmonic analysis branch is trained by preset higher harmonic frequency samples and corresponding error samples.

[0054] Furthermore, the system is also used to implement the following functions: The second measurement correction module includes a low sampling frequency switching branch, a low frequency self-calibration compensation branch and a basic harmonic analysis branch; wherein the low sampling frequency switching branch corresponds to a fixed low sampling frequency; the low frequency self-calibration compensation branch corresponds to a second preset frequency, and the second preset frequency is lower than the first preset frequency; the basic harmonic analysis branch performs harmonic component identification and measurement error analysis according to preset basic harmonics, wherein the preset basic harmonics include at least 3rd harmonics and 5th harmonics.

[0055] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0057] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An adaptive adjustment method for electric energy meter measurement, characterized in that: include: Establishing a first measurement mode and a second measurement mode for a target electric energy meter, wherein the first measurement mode is a high-precision mode and the second measurement mode is a low-energy consumption mode; Performing load fluctuation monitoring on the target electric energy meter through a load monitoring module to generate a load fluctuation index; If the load fluctuation index is greater than a preset fluctuation index, activating a first measurement correction module according to the first measurement mode to adjust the measurement accuracy of the target electric energy meter; If the load fluctuation index is less than or equal to a preset fluctuation index, the second measurement correction module is activated according to the second measurement mode to adjust the measurement accuracy of the target electric energy meter.

2. The method for adaptively adjusting electric energy meter measurement according to claim 1, characterized in that: The load fluctuation of the target electric energy meter is monitored by the load monitoring module to generate a load fluctuation index, including: The target electric energy meter is monitored by an integrated sensor in the load monitoring module to generate load fluctuation monitoring data; Wherein, the load fluctuation monitoring data includes voltage monitoring data, current monitoring data, active power monitoring data, reactive power monitoring data and power factor monitoring data; The voltage monitoring data, the current monitoring data, the active power monitoring data, the reactive power monitoring data and the power factor monitoring data are respectively calculated for fluctuation amplitude and then weightedly integrated to generate the load fluctuation index.

3. The method for self-adapting electric energy meter measurement according to claim 1, characterized in that: If the load fluctuation index is greater than a preset fluctuation index, activating a first measurement correction module according to the first measurement mode to adjust the measurement accuracy of the target electric energy meter includes: The first measurement correction module includes a high-frequency self-calibration compensation branch and a high-order harmonic analysis branch; Performing external environment parameter detection and measurement error influence analysis at a first preset frequency through the high-frequency self-calibration compensation branch to generate a first self-calibration error; The high-order harmonic analysis branch performs an electric energy meter measurement error analysis under high-order harmonics based on preset high-order harmonics to generate a first harmonic influence error; The measurement result of the target electric energy meter is error compensated by using the first self-calibration error and the first harmonic influence error to complete the measurement accuracy adjustment.

4. The method for self-adapting electric energy meter measurement according to claim 3, characterized in that: The high-frequency self-calibration compensation branch performs external environment parameter detection and measurement error influence analysis at a first preset frequency to generate a first self-calibration error, including: Performing external environment parameter detection according to the first preset frequency, wherein the external environment parameters include ambient temperature, ambient humidity, and ambient electromagnetic interference; Inputting the ambient temperature, the ambient humidity and the ambient electromagnetic interference into the high-frequency self-calibration compensation branch to perform environmental error impact analysis to generate the first self-calibration error; The high-frequency self-calibration compensation branch is trained by influencing samples through historical environmental errors.

5. The method for self-adapting electric energy meter measurement according to claim 3, characterized in that: The high-order harmonic analysis branch performs an electric energy meter measurement error analysis under high-order harmonics based on preset high-order harmonics to generate a first harmonic influence error, including: The first measurement correction module further includes a high sampling frequency switching branch, which is connected to a high-precision sampling frequency mapping table and is used to traverse the high-precision sampling frequency mapping table to perform high sampling frequency parameter matching and generate a high-frequency sampling frequency when the load fluctuation index is greater than a preset fluctuation index; The load monitoring module is controlled to collect voltage waveform and current waveform according to the high-frequency sampling frequency, and the voltage waveform and the current waveform are input into the high-order harmonic analysis branch to identify harmonic components and analyze measurement errors, thereby generating the first harmonic influence error.

6. The method for self-adapting electric energy meter measurement according to claim 5, characterized in that: Inputting the voltage waveform and the current waveform into the high-order harmonic analysis branch to perform harmonic component identification and measurement error analysis to generate the first harmonic influence error, including: Performing frequency domain signal transformation on the voltage waveform and the current waveform to generate a frequency domain voltage signal and a frequency domain current signal; Extracting the amplitude and phase corresponding to the preset higher harmonics according to the frequency domain voltage signal and the frequency domain current signal, and generating preset higher harmonic frequency components, wherein the preset higher harmonics include at least 3rd harmonic, 5th harmonic, 7th harmonic, 9th harmonic and 11th harmonic; Inputting the preset high-order harmonic frequency component into the high-order harmonic analysis branch to perform error analysis to generate the first harmonic influence error; The high-order harmonic analysis branch is trained by presetting high-order harmonic frequency samples and corresponding error samples.

7. The method for self-adapting electric energy meter measurement according to claim 6, characterized in that: If the load fluctuation index is less than or equal to the preset fluctuation index, activating the second measurement correction module according to the second measurement mode to adjust the measurement accuracy of the target electric energy meter includes: The second measurement correction module includes a low sampling frequency switching branch, a low frequency self-calibration compensation branch and a basic harmonic analysis branch; Among them, the low sampling frequency switching branch corresponds to a fixed low sampling frequency; the low frequency self-calibration compensation branch corresponds to a second preset frequency, and the second preset frequency is lower than the first preset frequency; the basic harmonic analysis branch performs harmonic component identification and measurement error analysis according to the preset basic harmonics, wherein the preset basic harmonics include at least 3rd harmonics and 5th harmonics.

8. An adaptive regulation system for electric energy meter measurement, characterized in that: The system comprises: A measurement mode establishing module, wherein the measurement mode establishing module establishes a first measurement mode and a second measurement mode for a target electric energy meter, wherein the first measurement mode is a high-precision mode and the second measurement mode is a low-energy consumption mode; A load fluctuation monitoring module, wherein the load fluctuation monitoring module monitors the load fluctuation of the target electric energy meter through a load monitoring module to generate a load fluctuation index; a first measurement accuracy adjustment module, the first measurement accuracy adjustment module being used to activate a first measurement correction module to adjust the measurement accuracy of the target electric energy meter according to the first measurement mode if the load fluctuation index is greater than a preset fluctuation index; The second measurement accuracy adjustment module is used to activate the second measurement correction module to adjust the measurement accuracy of the target electric energy meter according to the second measurement mode if the load fluctuation index is less than or equal to the preset fluctuation index.

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